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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Mark Porter, General Manager
Kevin Jernigan, Sr. Product Manager
Amazon Aurora PostgreSQL
Amazon RDS for PostgreSQL
January 16, 2017
PostgreSQL Compatibility
for Amazon Aurora
Agenda
 Why did we build Amazon Aurora?
 PostgreSQL Fast Facts
 Durability and Availability Architecture
 Performance Results vs. PostgreSQL
 Performance Architecture
 Announcing Performance Insights
 Feature Roadmap
 Preview Information & Questions
+
Why did we build Amazon Aurora, anyways?
Amazon Aurora with
PostgreSQL Compatibility
Multiple layers of
functionality all in a
monolithic stack
SQL
Transactions
Caching
Logging
Storage
Traditional relational databases are hard to scale
Traditional approaches to scale databases
Each architecture is limited by the monolithic mindset
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Application Application
SQL
Transactions
Caching
Logging
SQL
Transactions
Caching
Logging
Storage
Application
Storage Storage
SQL
Transactions
Caching
Logging
Storage
SQL
Transactions
Caching
Logging
Storage
 You would break apart the stack
 You would build something that:
 Can scale out…
 Is self-healing…
 Leverages distributed services…
Reimagining the relational database
What if you were inventing the database today?
A service-oriented architecture applied
to the database
1. Move the logging and storage layer into a
multitenant, scale-out, database-optimized
storage service
2. Integrate with other AWS services like
Amazon EC2, Amazon VPC, Amazon
DynamoDB, Amazon SWF, and Amazon
Route 53 for control & monitoring
3. Make it a managed service – using Amazon
RDS. Takes care of management and
administrative functions.
Amazon
DynamoDB
Amazon SWF
Amazon Route 53
Logging + Storage
SQL
Transactions
Caching
Amazon S3
Amazon RDS
In 2014, we launched Amazon Aurora with MySQL compatibility.
Now, we are adding PostgreSQL compatibility.
Customers can now choose how to use Amazon’s
cloud-optimized relational database, with the performance and
availability of commercial databases and the simplicity and cost-
effectiveness of open source databases.
It’s Time to Make Amazon Aurora Even Better
PostgreSQL Fast Facts
Amazon Aurora with
PostgreSQL Compatibility
 Open source database
 In active development for 20 years
 Owned by a foundation, not a single company
 Permissive innovation-friendly open source license
Open Source Initiative
PostgreSQL Fast Facts
PostgreSQL Fast Facts
 High performance out of the box
 Object-oriented and ANSI-SQL:2008 compatible
 Most geospatial features of any open-source database
 Supports stored procedures in 12 languages (Java, Perl, Python,
Ruby, Tcl, C/C++, its own Oracle-like PL/pgSQL, etc.)
Amazon Aurora Storage
What does PostgreSQL compatibility mean?
PostgreSQL 9.6 + Amazon Aurora cloud-optimized storage
 Performance: Up to 2x+ better performance than PostgreSQL alone
 Availability: failover time of < 30 seconds
 Durability: 6 copies across 3 Availability Zones
 Read Replicas: single-digit millisecond lag times on up to 15 replicas
AWS DMS
Amazon RDS
PostgreSQL
What does PostgreSQL compatibility mean? (Cont’d)
Cloud-native security and encryption
 AWS Key Management Service (KMS) and AWS Identity
and Access Management (IAM)
Easy to manage with Amazon RDS
Easy to load and unload
 AWS Database Migration Service and AWS Schema
Conversion Tool
Fully compatible with PostgreSQL, now and
for the foreseeable future
 Not a compatibility layer – native PostgreSQL
implementation
Durability & Availability
Amazon Aurora with
PostgreSQL Compatibility
Master Replica Replica Replica
Availability Zone 1
Shared Storage Volume – Transaction Aware
Primary
Database
Node
Read
Replica /
Secondary
Node
Read
Replica /
Secondary
Node
Read
Replica /
Secondary
Node
Availability Zone 2 Availability Zone 3
AWS Region
Storage
Monitoring
Database and
Instance
Monitoring
Scale-out, distributed, log structured storage
Amazon Aurora Storage Engine Overview
 Data is replicated 6 times across 3 Availability Zones
 Continuous backup to Amazon S3
 Continuous monitoring of nodes and disks for repair
 10GB segments as unit of repair or hotspot rebalance
 Quorum system for read/write; latency tolerant
 Quorum membership changes do not stall writes
 Storage volume automatically grows up to 64 TB
Optimizations
 4 out of 6 write quorum
 3 out of 6 read quorum
 Peer-to-peer replication for repairs
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
SQL
Transaction
AZ 1 AZ 2 AZ 3
Caching
What can fail?
 Segment failures (disks)
 Node failures (machines)
 AZ failures (network or datacenter)
Amazon Aurora Storage Engine Fault-tolerance
Availability
 Failing database nodes are
automatically detected and replaced
 Failing database processes are
automatically detected and recycled
 Replicas are automatically promoted
to primary if needed (failover)
 Customer specifiable fail-over order
Performance
 Customer applications can scale out read traffic across read replicas
 Read balancing across read replicas
Amazon Aurora Replicas
Segment snapshot Log records
Recovery point
Segment 1
Segment 2
Segment 3
Time
 Take periodic snapshot of each segment in parallel; stream the logs to Amazon S3
 Backup happens continuously without performance or availability impact
 At restore, retrieve the appropriate segment snapshots and log streams to storage nodes
 Apply log streams to segment snapshots in parallel and asynchronously
Amazon Aurora Continuous Backup
Traditional databases
 Have to replay logs since the last checkpoint
 Typically 5 minutes between checkpoints
 Single-threaded in MySQL and PostgreSQL;
requires a large number of disk accesses
Amazon Aurora
 No replay at startup because storage
system is transaction-aware
 Underlying storage replays log records
continuously, whether in recovery or not
 Coalescing is parallel, distributed, and
asynchronous
Checkpointed Data Log
Crash at T0 requires
a re-application of the
SQL in the log since
last checkpoint
T0 T0
Crash at T0 will result in logs being applied to
each segment on demand, in parallel,
asynchronously
Amazon Aurora Instant Crash Recovery
App
RunningFailure Detection DNS Propagation
Recovery
Database
Failure
Amazon RDS for PostgreSQL is good: failover times of ~60 seconds
Replica-Aware App Running
Failure Detection DNS Propagation
Recovery
Database
Failure
Amazon Aurora is better: failover times < 30 seconds
1 5 - 2 0 s e c
3 - 1 0 s e c
App
Running
Faster, more predictable failover with
Amazon Aurora
Performance vs. PostgreSQL
Amazon Aurora with
PostgreSQL Compatibility
PostgreSQL
Benchmark System Configurations
Amazon Aurora
AZ 1
EBS EBS EBS
45,000 total IOPS
AZ 1 AZ 2 AZ 3
Amazon S3
m4.16xlarge
database
instance
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Node
Storage
Node
c4.8xlarge
client driver
m4.16xlarge
database
instance
c4.8xlarge
client driver
ext4 filesystem
m4.16xlarge (64 VCPU, 256GiB), c4.8xlarge (36 VCPU, 60GiB)
Amazon Aurora is >=2x Faster on PgBench
pgbench “tpcb-like” workload, scale 2000 (30GiB). All configurations run for 60 minutes
SysBench oltp(write-only) workload with 30 GB database with 250 tables and 400,000 initial rows per table
Amazon Aurora is 2x-3x Faster on SysBench
Amazon Aurora delivers 2x the absolute peak of PostgreSQL
and 3x PostgreSQL performance at high client counts
OLTP test statistics:
queries performed:
read: 0
write: 432772903
other:(begin + commit) 216366749
total: 649139652
transactions: 108163671 (30044.73 per sec.)
read/write requests: 432772903 (120211.75 per sec.)
other operations: 216366749 (60100.40 per sec.)
ignored errors: 39407 (10.95 per sec.)
reconnects: 0 (0.00 per sec.)
Amazon Aurora: Over 120,000 Writes/Sec
Sustained sysbench throughput over 120K writes/sec
sysbench write-only 10GB workload with 250 tables and 25,000 initial rows per table. 10-minute warmup, 3,076 clients
Ignored errors are key constraint errors, designed into sysbench
Amazon Aurora Loads Data 3x Faster
Database initialization is three times faster than PostgreSQL
using the standard PgBench benchmark
Command: pgbench -i -s 2000 –F 90
Amazon Aurora Gives >2x Faster Response Times
Response time under heavy write load >2x faster than
PostgreSQL (and >10x more consistent)
SysBench oltp(write-only) 23GiB workload with 250 tables and 300,000 initial rows per table. 10-minute warmup.
While running at load, performance is more than three times
more consistent than PostgreSQL
Amazon Aurora Has More Consistent Throughput
PgBench “tpcb-like” workload at scale 2000. Amazon Aurora was run with 1280 clients. PostgreSQL was run with 512 clients (the concurrency
at which it delivered the best overall throughput)
Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks
Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks
Measurement Result
PgBench >= 2x faster
SysBench 2x-3x faster
Data Loading 3x faster
Response Time >2x faster
Throughput Jitter >3x more consistent
Throughput at Scale 3x faster
Recovery Speed Up to 85x faster
Amazon Aurora with PostgreSQL Compatibility
Performance By The Numbers
Performance Architecture
Amazon Aurora with
PostgreSQL Compatibility
 Do fewer IOs
 Minimize network packets
 Offload the database engine
DO LESS WORK
 Process asynchronously
 Reduce latency path
 Use lock-free data structures
 Batch operations together
BE MORE EFFICIENT
How Does Amazon Aurora Achieve
High Performance?
DATABASES ARE ALL ABOUT I/O
NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND
HIGH-THROUGHPUT PROCESSING NEEDS CPU AND MEMORY OPTIMIZATIONS
Write IO Traffic in Amazon RDS for PostgreSQL
WAL DATA COMMIT LOG & FILES
RDS FOR POSTGRESQL WITH MULTI-AZ
EBS mirrorEBS mirror
AZ 1 AZ 2
Amazon S3
EBS
Amazon Elastic
Block Store (EBS)
Primary
Database
Node
Standby
Database
Node
1
2
3
4
5
 Issue write to Amazon EBS, EBS issues to mirror,
acknowledge when both done
 Stage write to standby instance
 Issue write to EBS on standby instance
IO FLOW
 Steps 1, 3, 5 are sequential and synchronous
 This amplifies both latency and jitter
 Many types of writes for each user operation
OBSERVATIONS
T Y P E O F W R IT E
Write IO Traffic in an Amazon Aurora
Database Node
AZ 1 AZ 3
Primary
Database
Node
Amazon S3
AZ 2
Read
Replica /
Secondary
Node
AMAZON AURORA
ASYNC
4/6 QUORUM
DISTRIBUTED
WRITES
DATAAMAZON AURORA + WAL LOG COMMIT LOG & FILES
2x or better PostgreSQL Community Edition performance on
write-only or mixed read-write workloads
PERFORMANCE
WAL
T Y P E O F W R IT E
Read
Replica /
Secondary
Node
 Boxcar log records – fully ordered by LSN
 Shuffle to appropriate segments – partially ordered
 Boxcar to storage nodes and issue writes
IO FLOW
 Only write WAL records; all steps asynchronous
 No data block writes (checkpoint, cache replacement)
 6X more log writes, but 9X less network traffic
 Tolerant of network and storage outlier latency
OBSERVATIONS
Write IO Traffic in an Amazon Aurora
Storage Node
LOG RECORDS
Primary
Database
Node
INCOMING QUEUE
STORAGE NODE
AMAZON S3 BACKUP
1
2
3
4
5
6
7
8
UPDATE
QUEUE
ACK
HOT
LOG
DATA
BLOCKS
POINT IN TIME
SNAPSHOT
GC
SCRUB
COALESCE
SORT
GROUP
PEER TO PEER GOSSIPPeer
Storage
Nodes
All steps are asynchronous
Only steps 1 and 2 are in foreground latency path
Input queue is far smaller than PostgreSQL
Favors latency-sensitive operations
Uses disk space to buffer against spikes in activity
OBSERVATIONS
IO FLOW
① Receive record and add to in-memory queue
② Persist record and acknowledge
③ Organize records and identify gaps in log
④ Gossip with peers to fill in holes
⑤ Coalesce log records into new data block versions
⑥ Periodically stage log and new block versions to Amazon S3
⑦ Periodically garbage collect old versions
⑧ Periodically validate CRC codes on blocks
PAGE CACHE
UPDATE
Aurora Master
30% Read
70% Write
Aurora Replica
100% New Reads
Shared Multi-AZ Storage
PostgreSQL Master
30% Read
70% Write
PostgreSQL Replica
30% New Reads
70% Write
SINGLE-THREADED
WAL APPLY
Data Volume Data Volume
Physical:
 Ship redo (WAL) to Replica
 Write workload similar on both instances
 Independent storage
Physical:
 Ship redo (WAL) from Master to Replica
 Replica shares storage. No writes performed
 Cached pages have redo applied
 Advance read view when all commits seen
POSTGRESQL READ SCALING AMAZON AURORA READ SCALING
IO traffic in Aurora Replicas
 Cache normally lives inside the
operating system database
process– and goes away when/if
that database dies
 Aurora moves the cache out of
the database process
 Cache remains warm in the event
of a database restart
 Lets the database resume fully
loaded operations much faster
 Cache lives outside the database
process and remains warm
across database restarts
SQL
Transactions
Caching
SQL
Transactions
Caching
SQL
Transactions
Caching
RUNNING CRASH AND RESTART RUNNING
Applications Restart Faster With
Survivable Caches
Announcing Performance Insights
Amazon Aurora with
PostgreSQL Compatibility
 Released 2016
 O/S Metrics
 Process & thread List
 Up to 1 second granularity
First Step: Enhanced Monitoring
Next Step: Performance Insights
Database Engine
Performance Tuning
RDS is all about managed databases
Customers want performance managed too:
 Want easy tool for optimizing cloud database workloads
 May not have deep tuning expertise
 Want a single pane of glass to achieve this
Why Database Tuning?
 Automates sampling of data
 Exposes data via API
 Provides UI to show Database Load
Database Load:
Performance Insights at a glance
Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks
 Lock detection
 Execution plans
 API access
 Included with RDS
 35 days data retention
 Support for all RDS database engines in 2017
Beyond Measuring Database Load
Roadmap
Amazon Aurora with
PostgreSQL Compatibility
Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks
Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks
Timeline
 We are taking signups for the preview now
 We plan to release in general availability in 2017
How do I sign up for the preview?
https://pages.awscloud.com/amazon-aurora-with-postgresql-
compatibility-preview-form.html (<<< Clickable)
FAQs
https://aws.amazon.com/rds/aurora/faqs/#postgresql
Contact us:
 Mark Porter, General Manager (markpor@amazon.com)
 Kevin Jernigan, Senior Product Manager (kmj@amazon.com)
Questions
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank You!

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Announcing Amazon Aurora with PostgreSQL Compatibility - January 2017 AWS Online Tech Talks

  • 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Mark Porter, General Manager Kevin Jernigan, Sr. Product Manager Amazon Aurora PostgreSQL Amazon RDS for PostgreSQL January 16, 2017 PostgreSQL Compatibility for Amazon Aurora
  • 2. Agenda  Why did we build Amazon Aurora?  PostgreSQL Fast Facts  Durability and Availability Architecture  Performance Results vs. PostgreSQL  Performance Architecture  Announcing Performance Insights  Feature Roadmap  Preview Information & Questions +
  • 3. Why did we build Amazon Aurora, anyways? Amazon Aurora with PostgreSQL Compatibility
  • 4. Multiple layers of functionality all in a monolithic stack SQL Transactions Caching Logging Storage Traditional relational databases are hard to scale
  • 5. Traditional approaches to scale databases Each architecture is limited by the monolithic mindset SQL Transactions Caching Logging SQL Transactions Caching Logging Application Application SQL Transactions Caching Logging SQL Transactions Caching Logging Storage Application Storage Storage SQL Transactions Caching Logging Storage SQL Transactions Caching Logging Storage
  • 6.  You would break apart the stack  You would build something that:  Can scale out…  Is self-healing…  Leverages distributed services… Reimagining the relational database What if you were inventing the database today?
  • 7. A service-oriented architecture applied to the database 1. Move the logging and storage layer into a multitenant, scale-out, database-optimized storage service 2. Integrate with other AWS services like Amazon EC2, Amazon VPC, Amazon DynamoDB, Amazon SWF, and Amazon Route 53 for control & monitoring 3. Make it a managed service – using Amazon RDS. Takes care of management and administrative functions. Amazon DynamoDB Amazon SWF Amazon Route 53 Logging + Storage SQL Transactions Caching Amazon S3 Amazon RDS
  • 8. In 2014, we launched Amazon Aurora with MySQL compatibility. Now, we are adding PostgreSQL compatibility. Customers can now choose how to use Amazon’s cloud-optimized relational database, with the performance and availability of commercial databases and the simplicity and cost- effectiveness of open source databases. It’s Time to Make Amazon Aurora Even Better
  • 9. PostgreSQL Fast Facts Amazon Aurora with PostgreSQL Compatibility
  • 10.  Open source database  In active development for 20 years  Owned by a foundation, not a single company  Permissive innovation-friendly open source license Open Source Initiative PostgreSQL Fast Facts
  • 11. PostgreSQL Fast Facts  High performance out of the box  Object-oriented and ANSI-SQL:2008 compatible  Most geospatial features of any open-source database  Supports stored procedures in 12 languages (Java, Perl, Python, Ruby, Tcl, C/C++, its own Oracle-like PL/pgSQL, etc.)
  • 12. Amazon Aurora Storage What does PostgreSQL compatibility mean? PostgreSQL 9.6 + Amazon Aurora cloud-optimized storage  Performance: Up to 2x+ better performance than PostgreSQL alone  Availability: failover time of < 30 seconds  Durability: 6 copies across 3 Availability Zones  Read Replicas: single-digit millisecond lag times on up to 15 replicas
  • 13. AWS DMS Amazon RDS PostgreSQL What does PostgreSQL compatibility mean? (Cont’d) Cloud-native security and encryption  AWS Key Management Service (KMS) and AWS Identity and Access Management (IAM) Easy to manage with Amazon RDS Easy to load and unload  AWS Database Migration Service and AWS Schema Conversion Tool Fully compatible with PostgreSQL, now and for the foreseeable future  Not a compatibility layer – native PostgreSQL implementation
  • 14. Durability & Availability Amazon Aurora with PostgreSQL Compatibility
  • 15. Master Replica Replica Replica Availability Zone 1 Shared Storage Volume – Transaction Aware Primary Database Node Read Replica / Secondary Node Read Replica / Secondary Node Read Replica / Secondary Node Availability Zone 2 Availability Zone 3 AWS Region Storage Monitoring Database and Instance Monitoring Scale-out, distributed, log structured storage
  • 16. Amazon Aurora Storage Engine Overview  Data is replicated 6 times across 3 Availability Zones  Continuous backup to Amazon S3  Continuous monitoring of nodes and disks for repair  10GB segments as unit of repair or hotspot rebalance  Quorum system for read/write; latency tolerant  Quorum membership changes do not stall writes  Storage volume automatically grows up to 64 TB
  • 17. Optimizations  4 out of 6 write quorum  3 out of 6 read quorum  Peer-to-peer replication for repairs SQL Transaction AZ 1 AZ 2 AZ 3 Caching SQL Transaction AZ 1 AZ 2 AZ 3 Caching What can fail?  Segment failures (disks)  Node failures (machines)  AZ failures (network or datacenter) Amazon Aurora Storage Engine Fault-tolerance
  • 18. Availability  Failing database nodes are automatically detected and replaced  Failing database processes are automatically detected and recycled  Replicas are automatically promoted to primary if needed (failover)  Customer specifiable fail-over order Performance  Customer applications can scale out read traffic across read replicas  Read balancing across read replicas Amazon Aurora Replicas
  • 19. Segment snapshot Log records Recovery point Segment 1 Segment 2 Segment 3 Time  Take periodic snapshot of each segment in parallel; stream the logs to Amazon S3  Backup happens continuously without performance or availability impact  At restore, retrieve the appropriate segment snapshots and log streams to storage nodes  Apply log streams to segment snapshots in parallel and asynchronously Amazon Aurora Continuous Backup
  • 20. Traditional databases  Have to replay logs since the last checkpoint  Typically 5 minutes between checkpoints  Single-threaded in MySQL and PostgreSQL; requires a large number of disk accesses Amazon Aurora  No replay at startup because storage system is transaction-aware  Underlying storage replays log records continuously, whether in recovery or not  Coalescing is parallel, distributed, and asynchronous Checkpointed Data Log Crash at T0 requires a re-application of the SQL in the log since last checkpoint T0 T0 Crash at T0 will result in logs being applied to each segment on demand, in parallel, asynchronously Amazon Aurora Instant Crash Recovery
  • 21. App RunningFailure Detection DNS Propagation Recovery Database Failure Amazon RDS for PostgreSQL is good: failover times of ~60 seconds Replica-Aware App Running Failure Detection DNS Propagation Recovery Database Failure Amazon Aurora is better: failover times < 30 seconds 1 5 - 2 0 s e c 3 - 1 0 s e c App Running Faster, more predictable failover with Amazon Aurora
  • 22. Performance vs. PostgreSQL Amazon Aurora with PostgreSQL Compatibility
  • 23. PostgreSQL Benchmark System Configurations Amazon Aurora AZ 1 EBS EBS EBS 45,000 total IOPS AZ 1 AZ 2 AZ 3 Amazon S3 m4.16xlarge database instance Storage Node Storage Node Storage Node Storage Node Storage Node Storage Node c4.8xlarge client driver m4.16xlarge database instance c4.8xlarge client driver ext4 filesystem m4.16xlarge (64 VCPU, 256GiB), c4.8xlarge (36 VCPU, 60GiB)
  • 24. Amazon Aurora is >=2x Faster on PgBench pgbench “tpcb-like” workload, scale 2000 (30GiB). All configurations run for 60 minutes
  • 25. SysBench oltp(write-only) workload with 30 GB database with 250 tables and 400,000 initial rows per table Amazon Aurora is 2x-3x Faster on SysBench Amazon Aurora delivers 2x the absolute peak of PostgreSQL and 3x PostgreSQL performance at high client counts
  • 26. OLTP test statistics: queries performed: read: 0 write: 432772903 other:(begin + commit) 216366749 total: 649139652 transactions: 108163671 (30044.73 per sec.) read/write requests: 432772903 (120211.75 per sec.) other operations: 216366749 (60100.40 per sec.) ignored errors: 39407 (10.95 per sec.) reconnects: 0 (0.00 per sec.) Amazon Aurora: Over 120,000 Writes/Sec Sustained sysbench throughput over 120K writes/sec sysbench write-only 10GB workload with 250 tables and 25,000 initial rows per table. 10-minute warmup, 3,076 clients Ignored errors are key constraint errors, designed into sysbench
  • 27. Amazon Aurora Loads Data 3x Faster Database initialization is three times faster than PostgreSQL using the standard PgBench benchmark Command: pgbench -i -s 2000 –F 90
  • 28. Amazon Aurora Gives >2x Faster Response Times Response time under heavy write load >2x faster than PostgreSQL (and >10x more consistent) SysBench oltp(write-only) 23GiB workload with 250 tables and 300,000 initial rows per table. 10-minute warmup.
  • 29. While running at load, performance is more than three times more consistent than PostgreSQL Amazon Aurora Has More Consistent Throughput PgBench “tpcb-like” workload at scale 2000. Amazon Aurora was run with 1280 clients. PostgreSQL was run with 512 clients (the concurrency at which it delivered the best overall throughput)
  • 32. Measurement Result PgBench >= 2x faster SysBench 2x-3x faster Data Loading 3x faster Response Time >2x faster Throughput Jitter >3x more consistent Throughput at Scale 3x faster Recovery Speed Up to 85x faster Amazon Aurora with PostgreSQL Compatibility Performance By The Numbers
  • 33. Performance Architecture Amazon Aurora with PostgreSQL Compatibility
  • 34.  Do fewer IOs  Minimize network packets  Offload the database engine DO LESS WORK  Process asynchronously  Reduce latency path  Use lock-free data structures  Batch operations together BE MORE EFFICIENT How Does Amazon Aurora Achieve High Performance? DATABASES ARE ALL ABOUT I/O NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND HIGH-THROUGHPUT PROCESSING NEEDS CPU AND MEMORY OPTIMIZATIONS
  • 35. Write IO Traffic in Amazon RDS for PostgreSQL WAL DATA COMMIT LOG & FILES RDS FOR POSTGRESQL WITH MULTI-AZ EBS mirrorEBS mirror AZ 1 AZ 2 Amazon S3 EBS Amazon Elastic Block Store (EBS) Primary Database Node Standby Database Node 1 2 3 4 5  Issue write to Amazon EBS, EBS issues to mirror, acknowledge when both done  Stage write to standby instance  Issue write to EBS on standby instance IO FLOW  Steps 1, 3, 5 are sequential and synchronous  This amplifies both latency and jitter  Many types of writes for each user operation OBSERVATIONS T Y P E O F W R IT E
  • 36. Write IO Traffic in an Amazon Aurora Database Node AZ 1 AZ 3 Primary Database Node Amazon S3 AZ 2 Read Replica / Secondary Node AMAZON AURORA ASYNC 4/6 QUORUM DISTRIBUTED WRITES DATAAMAZON AURORA + WAL LOG COMMIT LOG & FILES 2x or better PostgreSQL Community Edition performance on write-only or mixed read-write workloads PERFORMANCE WAL T Y P E O F W R IT E Read Replica / Secondary Node  Boxcar log records – fully ordered by LSN  Shuffle to appropriate segments – partially ordered  Boxcar to storage nodes and issue writes IO FLOW  Only write WAL records; all steps asynchronous  No data block writes (checkpoint, cache replacement)  6X more log writes, but 9X less network traffic  Tolerant of network and storage outlier latency OBSERVATIONS
  • 37. Write IO Traffic in an Amazon Aurora Storage Node LOG RECORDS Primary Database Node INCOMING QUEUE STORAGE NODE AMAZON S3 BACKUP 1 2 3 4 5 6 7 8 UPDATE QUEUE ACK HOT LOG DATA BLOCKS POINT IN TIME SNAPSHOT GC SCRUB COALESCE SORT GROUP PEER TO PEER GOSSIPPeer Storage Nodes All steps are asynchronous Only steps 1 and 2 are in foreground latency path Input queue is far smaller than PostgreSQL Favors latency-sensitive operations Uses disk space to buffer against spikes in activity OBSERVATIONS IO FLOW ① Receive record and add to in-memory queue ② Persist record and acknowledge ③ Organize records and identify gaps in log ④ Gossip with peers to fill in holes ⑤ Coalesce log records into new data block versions ⑥ Periodically stage log and new block versions to Amazon S3 ⑦ Periodically garbage collect old versions ⑧ Periodically validate CRC codes on blocks
  • 38. PAGE CACHE UPDATE Aurora Master 30% Read 70% Write Aurora Replica 100% New Reads Shared Multi-AZ Storage PostgreSQL Master 30% Read 70% Write PostgreSQL Replica 30% New Reads 70% Write SINGLE-THREADED WAL APPLY Data Volume Data Volume Physical:  Ship redo (WAL) to Replica  Write workload similar on both instances  Independent storage Physical:  Ship redo (WAL) from Master to Replica  Replica shares storage. No writes performed  Cached pages have redo applied  Advance read view when all commits seen POSTGRESQL READ SCALING AMAZON AURORA READ SCALING IO traffic in Aurora Replicas
  • 39.  Cache normally lives inside the operating system database process– and goes away when/if that database dies  Aurora moves the cache out of the database process  Cache remains warm in the event of a database restart  Lets the database resume fully loaded operations much faster  Cache lives outside the database process and remains warm across database restarts SQL Transactions Caching SQL Transactions Caching SQL Transactions Caching RUNNING CRASH AND RESTART RUNNING Applications Restart Faster With Survivable Caches
  • 40. Announcing Performance Insights Amazon Aurora with PostgreSQL Compatibility
  • 41.  Released 2016  O/S Metrics  Process & thread List  Up to 1 second granularity First Step: Enhanced Monitoring
  • 42. Next Step: Performance Insights Database Engine Performance Tuning
  • 43. RDS is all about managed databases Customers want performance managed too:  Want easy tool for optimizing cloud database workloads  May not have deep tuning expertise  Want a single pane of glass to achieve this Why Database Tuning?
  • 44.  Automates sampling of data  Exposes data via API  Provides UI to show Database Load Database Load: Performance Insights at a glance
  • 46.  Lock detection  Execution plans  API access  Included with RDS  35 days data retention  Support for all RDS database engines in 2017 Beyond Measuring Database Load
  • 50. Timeline  We are taking signups for the preview now  We plan to release in general availability in 2017 How do I sign up for the preview? https://pages.awscloud.com/amazon-aurora-with-postgresql- compatibility-preview-form.html (<<< Clickable) FAQs https://aws.amazon.com/rds/aurora/faqs/#postgresql Contact us:  Mark Porter, General Manager (markpor@amazon.com)  Kevin Jernigan, Senior Product Manager (kmj@amazon.com) Questions
  • 51. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank You!